{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DGPlYumZnO1t"
   },
   "source": [
    "# Exploratory Data Analysis Using Python and BigQuery\n",
    "\n",
    "\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "1. Analyze a Pandas Dataframe\n",
    "2. Create Seaborn plots for Exploratory Data Analysis in Python \n",
    "3. Write a SQL query to pick up specific fields from a BigQuery dataset\n",
    "4. Exploratory Analysis in BigQuery\n",
    "\n",
    "\n",
    "## Introduction \n",
    "This lab is an introduction to linear regression using Python and Scikit-Learn.  This lab serves as a foundation for more complex algorithms and machine learning models that you will encounter in the course. We will train a linear regression model to predict housing price.\n",
    "\n",
    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/python.BQ_explore_data.ipynb) -- try to complete that notebook first before reviewing this solution notebook. \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AsHg6SD2nO1v"
   },
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the chown command to change the ownership\n",
    "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install the Google Cloud BigQuery library\n",
    "!pip install --user google-cloud-bigquery>=2.34.4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please ignore any incompatibility warnings and errors.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Restart** the kernel before proceeding further (On the Notebook menu - Kernel - Restart Kernel).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gEXV-RxPnO1w"
   },
   "outputs": [],
   "source": [
    "# You can use any Python source file as a module by executing an import statement in some other Python source file.\n",
    "# The import statement combines two operations; it searches for the named module, then it binds the results of that search\n",
    "# to a name in the local scope.\n",
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Import matplotlib to visualize the model\n",
    "import matplotlib.pyplot as plt\n",
    "# Seaborn is a Python data visualization library based on matplotlib\n",
    "import seaborn as sns\n",
    "%matplotlib inline   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dr2TkzKRnO1z"
   },
   "source": [
    "###  Load the Dataset\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we create a directory called usahousing.  This directory will hold the dataset that we copy from Google Cloud Storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a directory to hold the dataset\n",
    "if not os.path.isdir(\"../data/explore\"):\n",
    "    os.makedirs(\"../data/explore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we copy the Usahousing dataset from Google Cloud Storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying gs://cloud-training/mlongcp/v3.0_MLonGC/toy_data/housing_pre-proc_toy.csv...\n",
      "/ [1 files][138.8 KiB/138.8 KiB]                                                \n",
      "Operation completed over 1 objects/138.8 KiB.                                      \n"
     ]
    }
   ],
   "source": [
    "# Copy the file using `gcloud storage cp` from Google Cloud Storage in the required directory\n",    "!gcloud storage cp gs://cloud-training/mlongcp/v3.0_MLonGC/toy_data/housing_pre-proc_toy.csv ../data/explore  "   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we use the \"ls\" command to list files in the directory.  This ensures that the dataset was copied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 140\n",
      "-rw-r--r-- 1 jupyter jupyter 142150 Dec 24 11:35 housing_pre-proc_toy.csv\n"
     ]
    }
   ],
   "source": [
    "# `ls` shows the working directory's contents.  \n",
    "# The `l` flag list the all files with permissions and details\n",
    "!ls -l ../data/explore"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we read the dataset into a Pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CzrXJI8VnO10"
   },
   "outputs": [],
   "source": [
    "# TODO 1\n",
    "# Read a comma-separated values (csv) file into a DataFrame using the read_csv() function\n",
    "df_USAhousing = pd.read_csv('../data/explore/housing_pre-proc_toy.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Inspect the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "Y6VJQ1tdnO12",
    "outputId": "7a1d4eed-3e83-44a8-f495-a9b74444d3ec"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the first five rows using the head() method\n",
    "\n",
    "df_USAhousing.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for any null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# `isnull()` finds a null value in a column and `sum()` counts it\n",
    "df_USAhousing.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "nRTsvSzqnO17",
    "outputId": "f44ad14e-5fb4-4c70-e71c-9d149bca4869"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>-121.501836</td>\n",
       "      <td>1.015963</td>\n",
       "      <td>-124.3000</td>\n",
       "      <td>-122.200000</td>\n",
       "      <td>-122.0300</td>\n",
       "      <td>-120.697500</td>\n",
       "      <td>-118.9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>37.802288</td>\n",
       "      <td>0.803090</td>\n",
       "      <td>36.1300</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>37.8000</td>\n",
       "      <td>37.960000</td>\n",
       "      <td>41.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>30.088400</td>\n",
       "      <td>13.878416</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>2522.734000</td>\n",
       "      <td>1988.411988</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>1420.750000</td>\n",
       "      <td>2052.0000</td>\n",
       "      <td>3007.250000</td>\n",
       "      <td>28258.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>491.862400</td>\n",
       "      <td>362.499497</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>402.0000</td>\n",
       "      <td>581.250000</td>\n",
       "      <td>3864.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>1246.225200</td>\n",
       "      <td>925.075463</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>1030.5000</td>\n",
       "      <td>1488.250000</td>\n",
       "      <td>12203.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>458.122000</td>\n",
       "      <td>341.744308</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>374.5000</td>\n",
       "      <td>538.000000</td>\n",
       "      <td>3701.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>3.694312</td>\n",
       "      <td>1.859422</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>2.357875</td>\n",
       "      <td>3.2622</td>\n",
       "      <td>4.662975</td>\n",
       "      <td>15.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>170288.731200</td>\n",
       "      <td>97550.278529</td>\n",
       "      <td>22500.0000</td>\n",
       "      <td>92950.000000</td>\n",
       "      <td>150800.0000</td>\n",
       "      <td>219650.000000</td>\n",
       "      <td>500001.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count           mean           std         min  \\\n",
       "longitude           2500.0    -121.501836      1.015963   -124.3000   \n",
       "latitude            2500.0      37.802288      0.803090     36.1300   \n",
       "housing_median_age  2500.0      30.088400     13.878416      2.0000   \n",
       "total_rooms         2500.0    2522.734000   1988.411988     12.0000   \n",
       "total_bedrooms      2500.0     491.862400    362.499497      4.0000   \n",
       "population          2500.0    1246.225200    925.075463     18.0000   \n",
       "households          2500.0     458.122000    341.744308      2.0000   \n",
       "median_income       2500.0       3.694312      1.859422      0.4999   \n",
       "median_house_value  2500.0  170288.731200  97550.278529  22500.0000   \n",
       "\n",
       "                             25%          50%            75%          max  \n",
       "longitude            -122.200000    -122.0300    -120.697500    -118.9100  \n",
       "latitude               37.600000      37.8000      37.960000      41.9500  \n",
       "housing_median_age     18.000000      30.0000      41.000000      52.0000  \n",
       "total_rooms          1420.750000    2052.0000    3007.250000   28258.0000  \n",
       "total_bedrooms        282.000000     402.0000     581.250000    3864.0000  \n",
       "population            718.000000    1030.5000    1488.250000   12203.0000  \n",
       "households            263.000000     374.5000     538.000000    3701.0000  \n",
       "median_income           2.357875       3.2622       4.662975      15.0001  \n",
       "median_house_value  92950.000000  150800.0000  219650.000000  500001.0000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get some basic statistical details using describe() method\n",
    "df_stats = df_USAhousing.describe()\n",
    "# Transpose index and columns of the dataframe\n",
    "df_stats = df_stats.transpose()\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2500 entries, 0 to 2499\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           2500 non-null   float64\n",
      " 1   latitude            2500 non-null   float64\n",
      " 2   housing_median_age  2500 non-null   int64  \n",
      " 3   total_rooms         2500 non-null   int64  \n",
      " 4   total_bedrooms      2500 non-null   int64  \n",
      " 5   population          2500 non-null   int64  \n",
      " 6   households          2500 non-null   int64  \n",
      " 7   median_income       2500 non-null   float64\n",
      " 8   median_house_value  2500 non-null   int64  \n",
      " 9   ocean_proximity     2500 non-null   object \n",
      "dtypes: float64(3), int64(6), object(1)\n",
      "memory usage: 195.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get a concise summary of a DataFrame\n",
    "df_USAhousing.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a peek at the first and last five rows of the data for all columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows     :  2500\n",
      "Columns  :  10\n",
      "\n",
      "Features : \n",
      " ['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'median_house_value', 'ocean_proximity']\n",
      "\n",
      "Missing values :   0\n",
      "\n",
      "Unique values :  \n",
      "longitude              265\n",
      "latitude               275\n",
      "housing_median_age      51\n",
      "total_rooms           1889\n",
      "total_bedrooms         922\n",
      "population            1520\n",
      "households             890\n",
      "median_income         2186\n",
      "median_house_value    1553\n",
      "ocean_proximity          4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (\"Rows     : \" ,df_USAhousing.shape[0])\n",
    "print (\"Columns  : \" ,df_USAhousing.shape[1])\n",
    "print (\"\\nFeatures : \\n\" ,df_USAhousing.columns.tolist())\n",
    "print (\"\\nMissing values :  \", df_USAhousing.isnull().sum().values.sum())\n",
    "print (\"\\nUnique values :  \\n\",df_USAhousing\n",
    "       .nunique())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QWVdsrmgnO1_"
   },
   "source": [
    "## Explore the Data\n",
    "\n",
    "Let's create some simple plots to check out the data!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ba7c1dd8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# `heatmap` plots a rectangular data in a color-encoded matrix and\n",
    "# `corr` finds the pairwise correlation of all columns in the dataframe\n",
    "sns.heatmap(df_USAhousing.corr(numeric_only=True))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a displot showing \"median_house_value\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "SOsTLClWnO2B",
    "outputId": "b8a78674-5ddb-4706-90b4-37d7d83e8092"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ba6d15f8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 2a\n",
    "# Plot a univariate distribution of observations using seaborn `distplot()` function\n",
    "sns.displot(df_USAhousing['median_house_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'median_house_value')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the aesthetic style of the plots\n",
    "sns.set_style('whitegrid')\n",
    "# Plot a histogram using `hist()` function\n",
    "df_USAhousing['median_house_value'].hist(bins=30)\n",
    "plt.xlabel('median_house_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_USAhousing['median_income']\n",
    "y = df_USAhousing['median_house_value']\n",
    "\n",
    "# Scatter plot of y vs x using scatter() and `show()` display all open figures\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a jointplot showing \"median_income\"  versus \"median_house_value\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f25ba74a630>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 2b\n",
    "# `joinplot()` draws a plot of two variables with bivariate and univariate graphs.\n",
    "sns.jointplot(x='median_income',y='median_house_value',data=df_USAhousing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ba4ef400>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# `countplot()` shows the counts of observations in each categorical bin using bars\n",
    "sns.countplot(x = 'ocean_proximity', data=df_USAhousing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes numeric only?\n",
    "# plt.figure(figsize=(20,20))\n",
    "# Draw a multi-plot on every facet using `FacetGrid()`\n",
    "g = sns.FacetGrid(df_USAhousing, col=\"ocean_proximity\")\n",
    "# Pass a function and the name of one or more columns in the dataframe\n",
    "g.map(plt.hist, \"households\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes numeric only?\n",
    "# plt.figure(figsize=(20,20))\n",
    "# Draw a multi-plot on every facet using `FacetGrid()`\n",
    "g = sns.FacetGrid(df_USAhousing, col=\"ocean_proximity\")\n",
    "# Pass a function and the name of one or more columns in the dataframe\n",
    "g.map(plt.hist, \"median_income\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see below that this is the state of California!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_USAhousing['latitude']\n",
    "y = df_USAhousing['longitude']\n",
    "\n",
    "# Scatter plot of y vs x and display all open figures\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore and create ML datasets\n",
    "\n",
    "In this notebook, we will explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation tool. The idea is to suggest a likely fare to taxi riders so that they are not surprised, and so that they can protest if the charge is much higher than expected.\n",
    "\n",
    "## Learning objectives\n",
    "* Access and explore a public BigQuery dataset on NYC Taxi Cab rides\n",
    "* Visualize your dataset using the Seaborn library\n",
    "\n",
    "\n",
    "First, **restart the Kernel**.  Now, let's start with the Python imports that we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the python libraries\n",
    "from google.cloud import bigquery\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Extract sample data from BigQuery </h3>\n",
    "\n",
    "The dataset that we will use is <a href=\"https://console.cloud.google.com/bigquery?project=nyc-tlc&p=nyc-tlc&d=yellow&t=trips&page=table\">a BigQuery public dataset</a>. Click on the link, and look at the column names. Switch to the Details tab to verify that the number of records is one billion, and then switch to the Preview tab to look at a few rows.\n",
    "\n",
    "Let's write a SQL query to pick up interesting fields from the dataset. It's a good idea to get the timestamp in a predictable format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-05 01:20:05 UTC</td>\n",
       "      <td>-73.979935</td>\n",
       "      <td>40.761105</td>\n",
       "      <td>-73.966230</td>\n",
       "      <td>40.689831</td>\n",
       "      <td>1</td>\n",
       "      <td>84.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-03-07 00:58:45 UTC</td>\n",
       "      <td>-74.001449</td>\n",
       "      <td>40.726071</td>\n",
       "      <td>-73.980448</td>\n",
       "      <td>40.744253</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-03-05 20:17:51 UTC</td>\n",
       "      <td>-73.863740</td>\n",
       "      <td>40.734245</td>\n",
       "      <td>-73.991364</td>\n",
       "      <td>40.750096</td>\n",
       "      <td>1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-03-29 08:12:38 UTC</td>\n",
       "      <td>-73.993394</td>\n",
       "      <td>40.747158</td>\n",
       "      <td>-73.790150</td>\n",
       "      <td>40.646883</td>\n",
       "      <td>1</td>\n",
       "      <td>171.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-02-22 22:40:31 UTC</td>\n",
       "      <td>-73.937363</td>\n",
       "      <td>40.758041</td>\n",
       "      <td>-73.937386</td>\n",
       "      <td>40.758060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010-03-14 05:27:23 UTC</td>\n",
       "      <td>-73.993982</td>\n",
       "      <td>40.770577</td>\n",
       "      <td>-73.997214</td>\n",
       "      <td>40.762466</td>\n",
       "      <td>1</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2010-02-04 22:41:28 UTC</td>\n",
       "      <td>-73.991934</td>\n",
       "      <td>40.730339</td>\n",
       "      <td>-73.991934</td>\n",
       "      <td>40.730339</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-08-15 03:49:56 UTC</td>\n",
       "      <td>-73.937020</td>\n",
       "      <td>40.620175</td>\n",
       "      <td>-73.936452</td>\n",
       "      <td>40.620522</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010-03-02 14:45:23 UTC</td>\n",
       "      <td>-73.973403</td>\n",
       "      <td>40.754323</td>\n",
       "      <td>-73.806456</td>\n",
       "      <td>40.652384</td>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010-03-11 01:24:14 UTC</td>\n",
       "      <td>-73.990386</td>\n",
       "      <td>40.757301</td>\n",
       "      <td>-74.006484</td>\n",
       "      <td>40.782452</td>\n",
       "      <td>1</td>\n",
       "      <td>46.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0  2010-02-05 01:20:05 UTC        -73.979935        40.761105   \n",
       "1  2010-03-07 00:58:45 UTC        -74.001449        40.726071   \n",
       "2  2010-03-05 20:17:51 UTC        -73.863740        40.734245   \n",
       "3  2010-03-29 08:12:38 UTC        -73.993394        40.747158   \n",
       "4  2015-02-22 22:40:31 UTC        -73.937363        40.758041   \n",
       "5  2010-03-14 05:27:23 UTC        -73.993982        40.770577   \n",
       "6  2010-02-04 22:41:28 UTC        -73.991934        40.730339   \n",
       "7  2013-08-15 03:49:56 UTC        -73.937020        40.620175   \n",
       "8  2010-03-02 14:45:23 UTC        -73.973403        40.754323   \n",
       "9  2010-03-11 01:24:14 UTC        -73.990386        40.757301   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  trip_distance  \\\n",
       "0         -73.966230         40.689831                1           84.8   \n",
       "1         -73.980448         40.744253                2            2.2   \n",
       "2         -73.991364         40.750096                1            7.6   \n",
       "3         -73.790150         40.646883                1          171.1   \n",
       "4         -73.937386         40.758060                1            0.0   \n",
       "5         -73.997214         40.762466                1           12.4   \n",
       "6         -73.991934         40.730339                1            0.0   \n",
       "7         -73.936452         40.620522                1            0.0   \n",
       "8         -73.806456         40.652384                1           14.9   \n",
       "9         -74.006484         40.782452                1           46.4   \n",
       "\n",
       "   tolls_amount  fare_amount  total_amount  \n",
       "0           0.0          0.0           0.0  \n",
       "1           0.0          0.0           0.0  \n",
       "2           0.0          0.0           0.0  \n",
       "3           0.0          0.0           0.0  \n",
       "4           0.0          0.0           0.0  \n",
       "5           0.0          0.0           0.0  \n",
       "6           0.0          0.0           0.0  \n",
       "7           0.0          0.0           0.0  \n",
       "8           0.0          0.0           0.0  \n",
       "9           6.0          0.0           6.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "# SQL query to get a fields from dataset which prints the 10 records\n",
    "SELECT\n",
    "    FORMAT_TIMESTAMP(\n",
    "        \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
    "    pickup_longitude, pickup_latitude, dropoff_longitude,\n",
    "    dropoff_latitude, passenger_count, trip_distance, tolls_amount, \n",
    "    fare_amount, total_amount \n",
    "# TODO 3\n",
    "FROM\n",
    "    `nyc-tlc.yellow.trips`\n",
    "LIMIT 10"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's increase the number of records so that we can do some neat graphs.  There is no guarantee about the order in which records are returned, and so no guarantee about which records get returned if we simply increase the LIMIT. To properly sample the dataset, let's use the HASH of the pickup time and return 1 in 100,000 records -- because there are 1 billion records in the data, we should get back approximately 10,000 records if we do this.\n",
    "\n",
    "We will also store the BigQuery result in a Pandas dataframe named \"trips\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery trips\n",
    "SELECT\n",
    "    FORMAT_TIMESTAMP(\n",
    "        \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
    "    pickup_longitude, pickup_latitude, \n",
    "    dropoff_longitude, dropoff_latitude,\n",
    "    passenger_count,\n",
    "    trip_distance,\n",
    "    tolls_amount,\n",
    "    fare_amount,\n",
    "    total_amount\n",
    "FROM\n",
    "    `nyc-tlc.yellow.trips`\n",
    "WHERE\n",
    "    ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10789\n"
     ]
    }
   ],
   "source": [
    "print(len(trips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-05-17 15:15:00 UTC</td>\n",
       "      <td>-73.999550</td>\n",
       "      <td>40.760600</td>\n",
       "      <td>-73.999650</td>\n",
       "      <td>40.725220</td>\n",
       "      <td>1</td>\n",
       "      <td>5.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-21 11:53:00 UTC</td>\n",
       "      <td>-73.953860</td>\n",
       "      <td>40.787055</td>\n",
       "      <td>-73.972072</td>\n",
       "      <td>40.676780</td>\n",
       "      <td>6</td>\n",
       "      <td>11.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.7</td>\n",
       "      <td>38.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-09-25 03:47:00 UTC</td>\n",
       "      <td>-74.001323</td>\n",
       "      <td>40.727572</td>\n",
       "      <td>-73.987230</td>\n",
       "      <td>40.661257</td>\n",
       "      <td>1</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-10-06 15:16:00 UTC</td>\n",
       "      <td>-73.980130</td>\n",
       "      <td>40.760910</td>\n",
       "      <td>-73.861730</td>\n",
       "      <td>40.768330</td>\n",
       "      <td>2</td>\n",
       "      <td>11.47</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.5</td>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-08 21:50:00 UTC</td>\n",
       "      <td>-73.870867</td>\n",
       "      <td>40.773782</td>\n",
       "      <td>-74.003297</td>\n",
       "      <td>40.708215</td>\n",
       "      <td>2</td>\n",
       "      <td>11.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.5</td>\n",
       "      <td>37.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010-05-26 16:15:03 UTC</td>\n",
       "      <td>-74.002922</td>\n",
       "      <td>40.714474</td>\n",
       "      <td>-73.978505</td>\n",
       "      <td>40.758280</td>\n",
       "      <td>1</td>\n",
       "      <td>6.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.9</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-03-28 20:30:35 UTC</td>\n",
       "      <td>-73.973926</td>\n",
       "      <td>40.757725</td>\n",
       "      <td>-73.981695</td>\n",
       "      <td>40.761591</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-08-23 23:59:22 UTC</td>\n",
       "      <td>-73.783319</td>\n",
       "      <td>40.648480</td>\n",
       "      <td>-73.893649</td>\n",
       "      <td>40.646566</td>\n",
       "      <td>1</td>\n",
       "      <td>8.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-05-05 22:46:05 UTC</td>\n",
       "      <td>-74.009790</td>\n",
       "      <td>40.712483</td>\n",
       "      <td>-73.959293</td>\n",
       "      <td>40.768908</td>\n",
       "      <td>1</td>\n",
       "      <td>5.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.9</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010-12-21 13:08:00 UTC</td>\n",
       "      <td>-73.982422</td>\n",
       "      <td>40.739847</td>\n",
       "      <td>-73.981658</td>\n",
       "      <td>40.768732</td>\n",
       "      <td>2</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.9</td>\n",
       "      <td>15.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0  2014-05-17 15:15:00 UTC        -73.999550        40.760600   \n",
       "1  2012-02-21 11:53:00 UTC        -73.953860        40.787055   \n",
       "2  2009-09-25 03:47:00 UTC        -74.001323        40.727572   \n",
       "3  2014-10-06 15:16:00 UTC        -73.980130        40.760910   \n",
       "4  2014-12-08 21:50:00 UTC        -73.870867        40.773782   \n",
       "5  2010-05-26 16:15:03 UTC        -74.002922        40.714474   \n",
       "6  2009-03-28 20:30:35 UTC        -73.973926        40.757725   \n",
       "7  2009-08-23 23:59:22 UTC        -73.783319        40.648480   \n",
       "8  2012-05-05 22:46:05 UTC        -74.009790        40.712483   \n",
       "9  2010-12-21 13:08:00 UTC        -73.982422        40.739847   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  trip_distance  \\\n",
       "0         -73.999650         40.725220                1           5.57   \n",
       "1         -73.972072         40.676780                6          11.90   \n",
       "2         -73.987230         40.661257                1           5.76   \n",
       "3         -73.861730         40.768330                2          11.47   \n",
       "4         -74.003297         40.708215                2          11.81   \n",
       "5         -73.978505         40.758280                1           6.10   \n",
       "6         -73.981695         40.761591                1           0.50   \n",
       "7         -73.893649         40.646566                1           8.30   \n",
       "8         -73.959293         40.768908                1           5.20   \n",
       "9         -73.981658         40.768732                2           2.64   \n",
       "\n",
       "   tolls_amount  fare_amount  total_amount  \n",
       "0          0.00         31.0         33.50  \n",
       "1          0.00         31.7         38.54  \n",
       "2          0.00         16.9         17.40  \n",
       "3          5.33         36.5         47.33  \n",
       "4          0.00         33.5         37.50  \n",
       "5          0.00         20.9         25.76  \n",
       "6          0.00          4.6          4.60  \n",
       "7          0.00         18.9         18.90  \n",
       "8          0.00         16.9         19.90  \n",
       "9          0.00         14.9         15.40  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can slice Pandas dataframes as if they were arrays\n",
    "trips[:10]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Exploring data </h3>\n",
    "\n",
    "Let's explore this dataset and clean it up as necessary. We'll use the Python Seaborn package to visualize graphs and Pandas to do the slicing and filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 4\n",
    "# Use Seaborn `regplot()` function to plot the data and a linear regression model fit.\n",
    "ax = sns.regplot(\n",
    "    x=\"trip_distance\", y=\"fare_amount\",\n",
    "    fit_reg=False, ci=None, truncate=True, data=trips)\n",
    "ax.figure.set_size_inches(10, 8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm ... do you see something wrong with the data that needs addressing?\n",
    "\n",
    "It appears that we have a lot of invalid data that is being coded as zero distance and some fare amounts that are definitely illegitimate. Let's remove them from our analysis. We can do this by modifying the BigQuery query to keep only trips longer than zero miles and fare amounts that are at least the minimum cab fare ($2.50).\n",
    "\n",
    "Note the extra WHERE clauses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery trips\n",
    "# SQL query with where clause to save the results in the trips dataframe\n",
    "SELECT\n",
    "    FORMAT_TIMESTAMP(\n",
    "        \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
    "    pickup_longitude, pickup_latitude, \n",
    "    dropoff_longitude, dropoff_latitude,\n",
    "    passenger_count,\n",
    "    trip_distance,\n",
    "    tolls_amount,\n",
    "    fare_amount,\n",
    "    total_amount\n",
    "FROM\n",
    "    `nyc-tlc.yellow.trips`\n",
    "WHERE\n",
    "    ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1\n",
    "# TODO 4a\n",
    "    AND trip_distance > 0\n",
    "    AND fare_amount >= 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10716\n"
     ]
    }
   ],
   "source": [
    "print(len(trips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Seaborn `regplot()` function to plot the data and a linear regression model fit.\n",
    "ax = sns.regplot(\n",
    "    x=\"trip_distance\", y=\"fare_amount\",\n",
    "    fit_reg=False, ci=None, truncate=True, data=trips)\n",
    "ax.figure.set_size_inches(10, 8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's up with the streaks around 45 dollars and 50 dollars?  Those are fixed-amount rides from JFK and La Guardia airports into anywhere in Manhattan, i.e. to be expected. Let's list the data to make sure the values look reasonable.\n",
    "\n",
    "Let's also examine whether the toll amount is captured in the total amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2012-02-27 09:19:10 UTC</td>\n",
       "      <td>-73.874431</td>\n",
       "      <td>40.774011</td>\n",
       "      <td>-73.983967</td>\n",
       "      <td>40.744082</td>\n",
       "      <td>1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>27.7</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "40  2012-02-27 09:19:10 UTC        -73.874431        40.774011   \n",
       "\n",
       "    dropoff_longitude  dropoff_latitude  passenger_count  trip_distance  \\\n",
       "40         -73.983967         40.744082                1           11.6   \n",
       "\n",
       "    tolls_amount  fare_amount  total_amount  \n",
       "40           4.8         27.7          38.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tollrides = trips[trips[\"tolls_amount\"] > 0]\n",
    "tollrides[tollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2012-02-27 09:19:10 UTC</td>\n",
       "      <td>-73.972311</td>\n",
       "      <td>40.753067</td>\n",
       "      <td>-73.957389</td>\n",
       "      <td>40.817824</td>\n",
       "      <td>1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>22.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7799</th>\n",
       "      <td>2012-02-27 09:19:10 UTC</td>\n",
       "      <td>-73.987582</td>\n",
       "      <td>40.725468</td>\n",
       "      <td>-74.016628</td>\n",
       "      <td>40.715534</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>15.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10537</th>\n",
       "      <td>2012-02-27 09:19:10 UTC</td>\n",
       "      <td>-74.015483</td>\n",
       "      <td>40.715279</td>\n",
       "      <td>-73.998045</td>\n",
       "      <td>40.756273</td>\n",
       "      <td>1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "52     2012-02-27 09:19:10 UTC        -73.972311        40.753067   \n",
       "7799   2012-02-27 09:19:10 UTC        -73.987582        40.725468   \n",
       "10537  2012-02-27 09:19:10 UTC        -74.015483        40.715279   \n",
       "\n",
       "       dropoff_longitude  dropoff_latitude  passenger_count  trip_distance  \\\n",
       "52            -73.957389         40.817824                1            5.6   \n",
       "7799          -74.016628         40.715534                1            2.8   \n",
       "10537         -73.998045         40.756273                1            3.3   \n",
       "\n",
       "       tolls_amount  fare_amount  total_amount  \n",
       "52              0.0         16.9         22.62  \n",
       "7799            0.0         12.1         15.75  \n",
       "10537           0.0         10.9         13.40  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notollrides = trips[trips[\"tolls_amount\"] == 0]\n",
    "notollrides[notollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at a few samples above, it should be clear that the total amount reflects fare amount, toll and tip somewhat arbitrarily -- this is because when customers pay cash, the tip is not known.  So, we'll use the sum of fare_amount + tolls_amount as what needs to be predicted.  Tips are discretionary and do not have to be included in our fare estimation tool.\n",
    "\n",
    "Let's also look at the distribution of values within the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-72.602192</td>\n",
       "      <td>40.002372</td>\n",
       "      <td>-72.594838</td>\n",
       "      <td>40.002052</td>\n",
       "      <td>1.650056</td>\n",
       "      <td>2.856395</td>\n",
       "      <td>0.226428</td>\n",
       "      <td>11.109446</td>\n",
       "      <td>13.217078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.982373</td>\n",
       "      <td>5.474670</td>\n",
       "      <td>10.004324</td>\n",
       "      <td>5.474648</td>\n",
       "      <td>1.283577</td>\n",
       "      <td>3.322024</td>\n",
       "      <td>1.135934</td>\n",
       "      <td>9.137710</td>\n",
       "      <td>10.953156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-74.258183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-74.260472</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-73.992153</td>\n",
       "      <td>40.735936</td>\n",
       "      <td>-73.991566</td>\n",
       "      <td>40.734310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-73.981851</td>\n",
       "      <td>40.753264</td>\n",
       "      <td>-73.980373</td>\n",
       "      <td>40.752956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-73.967400</td>\n",
       "      <td>40.767340</td>\n",
       "      <td>-73.964142</td>\n",
       "      <td>40.767510</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>14.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.366138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.366138</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>179.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "count      10716.000000     10716.000000       10716.000000      10716.000000   \n",
       "mean         -72.602192        40.002372         -72.594838         40.002052   \n",
       "std            9.982373         5.474670          10.004324          5.474648   \n",
       "min          -74.258183         0.000000         -74.260472          0.000000   \n",
       "25%          -73.992153        40.735936         -73.991566         40.734310   \n",
       "50%          -73.981851        40.753264         -73.980373         40.752956   \n",
       "75%          -73.967400        40.767340         -73.964142         40.767510   \n",
       "max            0.000000        41.366138           0.000000         41.366138   \n",
       "\n",
       "       passenger_count  trip_distance  tolls_amount   fare_amount  \\\n",
       "count     10716.000000   10716.000000  10716.000000  10716.000000   \n",
       "mean          1.650056       2.856395      0.226428     11.109446   \n",
       "std           1.283577       3.322024      1.135934      9.137710   \n",
       "min           0.000000       0.010000      0.000000      2.500000   \n",
       "25%           1.000000       1.040000      0.000000      6.000000   \n",
       "50%           1.000000       1.770000      0.000000      8.500000   \n",
       "75%           2.000000       3.160000      0.000000     12.500000   \n",
       "max           6.000000      42.800000     16.000000    179.000000   \n",
       "\n",
       "       total_amount  \n",
       "count  10716.000000  \n",
       "mean      13.217078  \n",
       "std       10.953156  \n",
       "min        2.500000  \n",
       "25%        7.300000  \n",
       "50%       10.000000  \n",
       "75%       14.600000  \n",
       "max      179.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the distribution of values within the columns using `describe()`\n",
    "trips.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2021 Google Inc.  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
    "http://www.apache.org/licenses/LICENSE-2.0\n",
    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
   ]
  }
 ],
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